Senior Data Engineer
Posted 58ds ago
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Job Description
Senior Data Engineer responsible for building robust data pipelines and aggregates for Zeta's AdTech platform. Collaborating with backend, ML, and product teams to deliver reliable data.
Responsibilities:
- Build data pipelines: Develop robust batch and streaming pipelines (Kafka/Kinesis) to ingest, transform, and enrich large-scale event data (impressions, clicks, conversions, costs, identity signals).
- Create data aggregates & marts: Design and maintain curated aggregates and dimensional models for multiple consumers—prediction models, agents, BI dashboards, and measurement workflows.
- Data modeling & contracts: Define schemas, data contracts, and versioning strategies to keep downstream systems stable as sources evolve.
- Data quality & reliability: Implement validation, anomaly detection, backfills, and reconciliation to ensure completeness, correctness, and timeliness (SLAs/SLOs).
- Performance & cost optimization: Optimize compute/storage for scale (partitioning, file sizing, incremental processing, indexing), balancing latency, throughput, and cost.
- Orchestration & automation: Build repeatable workflows with scheduling/orchestration (e.g., Airflow, Dagster, Step Functions) and CI/CD for data pipelines.
- Observability for data systems: Instrument pipelines with metrics, logs, lineage, and alerting to accelerate detection and root-cause analysis of data issues.
- Security & governance: Apply least-privilege access, PII-aware handling, and governance controls aligned with enterprise standards.
Requirements:
- 5+ years building production data pipelines and data products (batch and/or streaming) in a high-scale environment.
- Strong experience with SQL and data modeling (dimensional modeling, star/snowflake schemas, event modeling).
- Hands-on experience with streaming systems (Kafka preferred) and/or AWS Kinesis, including event-driven designs.
- Proficiency in one or more languages used for data engineering (Python, Java, Scala, or Go).
- Experience with distributed data processing (Spark, Flink, or equivalent) and performance tuning at scale.
- Experience with AWS data services and cloud-native patterns (S3, Glue/EMR, Athena, Redshift, etc. as applicable).
- Familiarity with lakehouse/table formats and large-scale storage patterns (e.g., Parquet; Iceberg/Hudi/Delta are a plus).
- Experience with orchestration/workflow tooling (Airflow/Dagster/Step Functions) and CI/CD for data workloads.
- Strong data quality/observability practices (tests, monitoring, lineage; understanding of SLAs/SLOs).
- Experience with SQL + NoSQL data stores (e.g., Postgres/MySQL; DynamoDB/Cassandra/Redis) and choosing the right store per use case.
- Clear communicator and collaborator; able to work with mixed audiences and translate needs into reliable data interfaces.
Benefits:
- Unlimited PTO
- Excellent medical, dental, and vision coverage
- Employee Equity
- Employee Discounts, Virtual Wellness Classes, and Pet Insurance And more!!



















